Speaker Authentication and Validation Using ECAPA-TDNN Speaker Embeddings

Le résumé fourni par la source

This study introduces an approach for fine-tuning pre-trained ECAPA-TDNN models to compute embedding scores for speaker verification tasks. These embeddings are applied to a binary classification problem, in order to distinguish whether two audio clips belong to the same speaker or to different speakers by identifying an optimal similarity threshold. The model generates a high similarity score for audio samples from the same speaker and a significantly lower score for those from different speakers. The methodology involved gathering data from a diverse set of participants and using machine learning techniques to determine the optimal threshold value, which was subsequently validated on a separate test dataset.

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